A growing number of studies in both the scientific and the philosophical literature have drawn on a Bayesian predictive processing framework to account for the formation of delusions. The key here is that delusions form because of disrupted prediction error signalling. Parrott’s recent critique argues that the framework is incomplete in two respects: it leaves unclear why delusional hypotheses are selected over none at all or over more plausible alternatives; it leaves unclear how exactly it is that delusional hypotheses are (...) generated in the first place. In this paper, we defend the framework’s explanatory power by way of showing how it can go a long way in helping disentangle these concerns. We take up each of them in turn. As for the first, our argument turns on two claims: a) believing nothing, although possible in principle, may not be a viable procedure in the circumstances; b) alternative hypotheses may be rendered unavailable for selection, due to aberrant prediction errors. As for the second concern, we distinguish three ways in which delusional hypotheses may be initially acquired, all of which are based on experience broadly construed. (shrink)

The predictive processing account of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. However, its promises of grand unification will remain unfulfilled unless the account becomes theoretically robust. In this paper, we focus on empirical commitments of PP, since they are necessary both for its theoretical status to be established and for explanations of individual phenomena to be falsifiable. First, we argue that PP is a varied research tradition, which may employ (...) various kinds of scientific representations, differing in the scope of empirical commitments they entail. Two major perspectives on PP qua cognitive theory may then be distinguished: generalized vs. hierarchical. The first one fails to provide empirical detail, and the latter constrains possible physical implementations. However, we show that even hierarchical PP is insufficiently restrictive to disallow incorrect models and may be adjusted to explain any neurocognitive phenomenon–including non-existent or impossible ones–through flexible adjustments. This renders PP a universal modeling tool with an unrestricted number of degrees of freedom. Therefore, in contrast with declarations of its proponents, it should not be understood as a unifying theoretical perspective, but as a computational framework, possibly informing further theory development in cognitive science. (shrink)

This paper considers the question of whether the influential distinction between levels of explanation introduced by David Marr can be used as a general framework for contemplating levels of explanation in cognitive sciences. Marr introduced three levels at which we can explain cognitive processes: the computational, algorithmic, and implementational levels. Some argue that Marr’s levels of explanation can only be applied to modular cognitive systems. However, since many psychological processes are non-modular, it seems that Marr’s levels of explanation cannot explain (...) such psychological processes. To show that the latter claim is not convincing, the paper draws upon an influential paradigm from cognitive sciences that is based on the principle of free energy. Based on this paradigm, the paper argues that even non-modular psychological processes can be computationally analyzed and algorithmically implemented. The conclusion of the paper is that, at least under the assumption that the func- tion of the mind is to minimize free energy, Marr’s levels of explanation can be successfully used as a general framework for understanding psychological processes at different levels of description. (shrink)